There’s a growing understanding that machine learning and artificial intelligence (AI), while often talked about as buzzwords, are fundamentally about data. At the core, it’s all about how data is analysed and how decisions are made based on that analysis. There’s been a shift from fearing these technologies—due to uncertainty about how they work—to recognising the value they can bring to everyday operations.

In logistics specifically, companies are increasingly using data, machine learning, and AI not just as trendy labels but as concrete tools that enhance customer experience, improve internal efficiency, and reduce errors. These technologies are proving to make life easier, particularly in serving customers, by freeing up time for deeper engagement and understanding of their needs.

“If a process that once required a lengthy phone call, like arranging insurance, can now be completed in 10-20 seconds, it allows more time to focus on what really matters—understanding where shipments are and what customers truly need,” Matthew Phillips, Breeze’s Chief Commercial Officer, said. 

Breeze, a fully automated and digital insurance solution for freight forwarders, leveraging innovative technology to protect customers’ cargo, was launched, based on the team’s time working in the sector, to provide an online platform for instant insurance quotes. 

“The more people realise that AI and machine learning are here to assist rather than threaten, the more they can leverage these technologies to bring about positive changes. While there are concerns about robots in other industries, in logistics, these advancements can lead to significant improvements.”

Powerful potential

The industry is now seeing the impact of various changes — like capacity shifts and geopolitical developments — reflected in the data. Before shipping or insuring cargo, an algorithm could analyse the current situation and suggest alternatives, like using a train or aeroplane, if it detects that ports are overcrowded or routes are congested.

The ability to plan alternatives is crucial. If something unexpected happens, companies can rely on models that identify a broad range of opportunities, whether it’s finding better rates or exploring different routes. For example, if Hong Kong is becoming increasingly busy, a model might suggest considering Tokyo, Vietnam, or another location. It can also help figure out the logistics of moving goods from one place to another.

“What once required numerous phone calls or emails can now be streamlined—just input your current location and destination and the system handles the rest. This process isn’t static; rates and risks change frequently, whether they’re related to insurance or shipping, and these changes often happen for specific reasons,” Phillips outlined.

“Traditionally, someone might analyse a few key data points to decide that rates should increase due to rising risks.

“However, with data, machine learning, and AI, the industry can now make these decisions more frequently, with greater detail, and with more accuracy. Instead of relying on just a few data points, these technologies consider a wide range of factors, making rate-setting and risk assessment far more precise.

“Importantly, this process doesn’t have to be a “black box”—you can understand exactly what’s happening behind the scenes. This approach not only makes businesses more profitable but also offers better protection for customers.”

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